Technology and big data are rapidly changing every part of our lives, and the healthcare industry is no exception. From enhancing the efficacy of medical research projects to the delivery of better patient care after a procedure, many advancements in the healthcare system stem from business analytics at work in the healthcare industry.
For business analysts who want to support the optimization of medical care and a healthcare system that anticipates and meets patients’ needs, the opportunities for leveraging data to do just that are many. Those opportunities are also growing—the Bureau of Labor Statistics projects that healthcare occupations will grow 15% between 2019 and 2029. Management analyst positions, including business analysts, are expected to grow 11% in the same time frame.
Business analytics empower healthcare systems to make more strategic decisions, detect diseases earlier, improve patient outcomes, and much more. In this blog, we will share ten ways that business analytics is changing the healthcare industry.
Business analytics are a natural and strong fit for the healthcare industry when it comes to research and development. As doctors, pharmacists, and scientists conduct research and develop theories to improve patient health, insights from big data can lead to breakthroughs and increase efficiency.
Take the Cancer Moonshot program, for example. President Obama launched Cancer Moonshot to “accelerate scientific discovery in cancer, foster greater collaboration, and improve the sharing of data.” Moonshot’s “radical data sharing” approach requires that all findings be accessible to anyone at no cost. Their Cancer Research Data Commons (CRDC) infrastructure empowers users to access, analyze, add, and share data. Such data sharing has, in part, contributed to the acceleration of “the discovery of new immune targets for cancer treatments.”
Data analytics save both doctors and patients time and money by equipping researchers to better study which medications work best for various diseases and demographics. For example, researchers devised a machine learning algorithm to recognize and forecast the ways that drugs may have adverse effects in men and women. The algorithm analyzed FDA reports spanning five decades—something that would be nearly impossible for a researcher or team of researchers to do with any efficiency. By filtering for differences in biological sex, researchers can better get to the root of why there are adverse drug effects and how they may specifically arise in some people, thereby enabling doctors to skip drugs that are likely to cause problems in certain patients and save time, money, and suffering by prescribing a drug less likely to cause harm.
The pharmaceutical industry has seen a range of improvements stemming from business analytics changing healthcare. Excellent data analysis has improved operations, profits, and decision-making in the pharmaceutical realm. Data analysis has also increased the rate of research in developing new medicine, which lowers medicine costs by reducing hours spent researching. Predictive modeling also empowers researchers to analyze “toxicity, interactions, and inhibition of the drug,” bypassing the repetitive process of physically testing drugs on plants or animals.
These increases in efficiency are not just good news for pharmaceutical companies or researchers eager to move to a new project. It’s also wonderful news for patients in need of life-saving medical care. That is the beauty of business analytics in healthcare—it doesn’t just improve the bottom line (though it does, and mightily). It improves patient care and contributes to the alleviation of human suffering.
Tracking and Improving Wellness
Business analytics has equipped both individuals and healthcare providers to better track and improve health and wellness. Technologies like smartwatches and mobile apps empower patients to take greater ownership over their health by monitoring key factors like sleep, water intake, and exercise. The IQVIA Institute for Human Data Science found that “the use of health apps and wearable devices could potentially save the U.S. healthcare system $7 billion per year.”
Some health apps also empower healthcare providers to track patient wellness data, encouraging open communication between patient and provider beyond the dialogue that takes place at medical appointments. One study found that sharing health and wellness information between medical appointments can:
- Promote patient engagement
- Improve patient-centered care
- Enhance patient-provider collaboration
As patients share their health and wellness data with their providers via apps, the chances are high that their experience and outcomes as patients will improve. Patients who are content with their healthcare providers and the outcomes they are experiencing help reduce the cost of healthcare and benefit public health. Again we see that business analytics benefit the healthcare system as a whole, from providers and administrators to patients.
Bettering Follow-up Care
Through big data and business analytics, health plans are able to identify and categorize patients according to their personal health factors. Researchers have found that high-risk patients are less likely to follow up with healthcare providers or fill prescriptions upon discharge from the hospital. Based on the insights revealed by this data, providers treating such a patient can implement plans and education that motivate the patient to follow through with treatment after they have left the hospital.
Completing courses of medicine and keeping up with follow-up visits and therapies can help patients avoid readmissions to the hospital or further health problems. This leads to greater quality of life for the patient and lower cost of healthcare because big data and business analytics were applied to the healthcare sector.
Brian Coffey, senior vice president and chief data insight and innovation at Southwestern Health Resources, explained it to Health IT Analytics as follows:
“Using [Electronic Health Record] data, we can monitor patients admitted to the emergency department. We can track their length of stay throughout that encounter, and then ensure that the patient has a follow-up with their PCP. We can do all of this even before the claims data drops, because we can see it in the EHR and the registry data.”
These efficiencies and improved patient treatment plans made possible by business analytics benefit both the patient and the provider by enhancing clarity and care for everyone in the healthcare system.
Avoiding Unnecessary Medical Interventions
Business analytics strengthen the healthcare industry by making shared patient records possible. For example, many emergency rooms now have access to the same pool of patient records. This shared information helps healthcare providers to run tests and propose treatment plans that build on prior medical visits, rather than repeating interventions that have been performed in the past.
This not only benefits patients who are more likely to receive effective treatment, but it greatly increases efficiencies in the healthcare system as well. When patients receive the care and follow-up they need, they no longer need to frequent the emergency room. This breaks the cycle of iterative tests and attempts at intervention, empowering the patient to improve and the healthcare system to function more optimally.
Predicting and Detecting Diseases
Disease prediction and detection showcases one of the purposes of big data and business analytics in healthcare: predicting and solving problems by using data-driven methods. As researchers leverage machine learning, statistical applications, and sophisticated calculations to massive amounts of health data, they discover key insights and patterns that inform useful predictions.
Here are just a few examples of business and data analytics changing healthcare by predicting diseases:
- Through analysis of insurance and pharmacy data, Fuzzy Logix data analysts have identified 742 risk factors that predict whether or not someone is at risk for opioid abuse
- Machine learning algorithms have been developed to detect or predict heart diseases, kidney diseases, breast cancer, and Parkinson’s disease.
- A predictive analytics model discovered VGF, a protein that protects the brain against Alzheimer’s disease
- Insights from the analysis of large data sets have improved diagnosis and prediction of diseases including diabetes mellitus, mental health disorders, and suicidal behavior.
Every day, data analysts are working on new models, systems and applications that will highlight big data’s ability to improve disease prediction and detections. Whether analyzing for common genes or mutations, shared symptoms, or other factors yet to be discovered, the ability to analyze large data sets efficiently, accurately, and in a way that produces useful insights will only continue to transform and enhance the healthcare sector.
Optimizing Emergency Room Staffing
Applying business analytics in the hospital setting helps administrators and healthcare providers improve patient experiences and increase efficiency in the part of the hospital that’s typically thought of as chaotic and slow—the emergency room. Data analysis enables hospital providers to look at large swaths of historical data in detail so they can ensure proper staffing, reduce wait times for patients, and improve triage.
Kirk Jensen, an M.D. who is the chief innovation officer for Envision Physician Services, says that Envision’s data management tool evaluates key emergency room factors such as:
- Length of stay
- Impact of visit
- Hour of the day
- Day of the week
- Season of the year
- Variances in healthcare provider productivity
“We’re marrying science and analytics with patient care and clinician needs, and there’s a tremendous opportunity here to optimize safety and make service improvements,” Jensen told Health Tech Magazine. “Once we have all of this data in the system, the next iteration is adding the artificial intelligence component and data mining to see how much further we can optimize this.”
As Jensen makes clear, business analytics has achieved great strides in the healthcare space, while also illuminating just how much more there is to explore and discover.
Improving Mental Healthcare
While people often struggle to discuss their mental health concerns in face-to-face conversations, there has been a significant rise in talking about such topics as anxiety and depression in the digital space. Despite significant considerations and challenges like privacy, some researchers have been able to leverage business analytics to better predict psychological wellness.
For example, one study found that social media is a valuable data source for understanding psychological moods and behaviors.
“In the future,” the study reads, connecting Twitter [application program interface] with Python, then applying sentimental analysis on ‘posts,’ ‘liked pages’, ‘followed pages,’ and ‘comments’ of the Twitter user will provide a cost-eﬀective way to detect depression for target patients.”
A machine learning tool has also been developed to analyze electronic health record data to determine a patient’s risk for suicidal attempts. The machine learning tool, which the patient experiences as a screening tool, asks about factors like suicidal thoughts, relationships, difficulty concentrating, trouble sleeping, and feeling agitated. By collecting data across several factors rather than simply asking about suicidal thoughts or suicidal ideation, mental health providers are better able to determine a patient’s risk status and address their needs.
Reducing the Risk of Surgical Complications
Patient registries made possible by big data and business analytics are helping the healthcare industry and patients avoid negative outcomes after surgical procedures. The Michigan Bariatric Surgery Collective (MBSC), for example, used a patient registry to improve their bariatric surgery procedures. Implementing this registry platform resulted in a stunning 67% decrease in post-surgical deaths, a 20% decrease in readmissions, and a 44% decrease in overall complication rates. These results led to $34.9 million in statewide savings for the state of Michigan and saved insurance provider Blue Cross Blue Shield of Michigan $11.8 million over five years.
Another data analysis tool predicts a patient’s risk factor for post-surgical complications. The tool, which was intentionally designed to be practical and useful for patient care, has been used to predict complications in two major ways—acute kidney injury and strokes, cardiac events, or death within a month of a patient having had surgery. This information empowers researchers and medical providers to investigate risk reduction and improve the delivery of care to surgical patients based on their risk factors.
Optimizing Health Insurance Delivery
Security and anti-fraud technologies are a priority of the business analytics community, which has major implications for patient record and health insurance companies who deal in confidential information. Business analytics solutions can prevent and detect fraud using accurate, iterative processes that reduce instances of human error or oversight. In just one year, the Centers for Medicare and Medicaid Services used predictive analytics to identify or prevent over $210 million in healthcare fraud.
Business analytics can also improve other dimensions of the health insurance industry, including:
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